





from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
    from ..models.auto import AutoTokenizer





class 					A__       :




   """simple docstring"""



   def 				__lowercase						(   self   ,    lowercase)     ->		Any:







          '''simple docstring'''

          raise NotImplementedError()

   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          raise NotImplementedError()





class 					A__       (				__lowercase      ):




   """simple docstring"""



   def __init__(   self   ,    lowercase   ,    lowercase = False   ,    **lowercase)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      int					=							tokenizer
          a__		:      Any					=							skip_prompt
          a__		:      Union[str, Any]					=							decode_kwargs

          # variables used in the streaming process
          a__		:      Union[str, Any]					=							[]
          a__		:      str					=							0
          a__		:      str					=							True

   def 				__lowercase						(   self   ,    lowercase)     ->		Any:







          '''simple docstring'''

          if len(value.shape) > 1 and value.shape[0] > 1:
                 raise ValueError('TextStreamer only supports batch size 1')
          elif len(value.shape) > 1:
                 a__		:      Optional[int]					=							value[0]

          if self.skip_prompt and self.next_tokens_are_prompt:
                 a__		:      Optional[Any]					=							False
                 return

          # Add the new token to the cache and decodes the entire thing.
          self.token_cache.extend(value.tolist())
          a__		:      Optional[int]					=							self.tokenizer.decode(self.token_cache   ,    **self.decode_kwargs)

          # After the symbol for a new line, we flush the cache.
          if text.endswith('\n'):
                 a__		:      Optional[int]					=							text[self.print_len :]
                 a__		:      Dict					=							[]
                 a__		:      int					=							0
          # If the last token is a CJK character, we print the characters.
          elif len(lowercase) > 0 and self._is_chinese_char(ord(text[-1])):
                 a__		:      Optional[int]					=							text[self.print_len :]
                 self.print_len += len(lowercase)
          # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
          # which may change with the subsequent token -- there are probably smarter ways to do this!)
          else:
                 a__		:      Any					=							text[self.print_len : text.rfind(' ') + 1]
                 self.print_len += len(lowercase)

          self.on_finalized_text(lowercase)

   def 				__lowercase						(   self)     ->		List[str]:







          '''simple docstring'''

          if len(self.token_cache) > 0:
                 a__		:      str					=							self.tokenizer.decode(self.token_cache   ,    **self.decode_kwargs)
                 a__		:      str					=							text[self.print_len :]
                 a__		:      str					=							[]
                 a__		:      int					=							0
          else:
                 a__		:      int					=							""

          a__		:      Optional[Any]					=							True
          self.on_finalized_text(lowercase   ,    stream_end=lowercase)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = False)     ->		str:







          '''simple docstring'''

          print(lowercase   ,    flush=lowercase   ,    end='' if not stream_end else None)

   def 				__lowercase						(   self   ,    lowercase)     ->		str:







          '''simple docstring'''

          if (
              (cp >= 0x4_e00 and cp <= 0x9_fff)
              or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
              or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
              or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
              or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
              or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
              or (cp >= 0xf_900 and cp <= 0xf_aff)
              or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
          ):  #
                 return True

          return False





class 					A__       (				__lowercase      ):




   """simple docstring"""



   def __init__(   self   ,    lowercase   ,    lowercase = False   ,    lowercase = None   ,    **lowercase)     ->		str:







          '''simple docstring'''

          super().__init__(lowercase   ,    lowercase   ,    **lowercase)
          a__		:      str					=							Queue()
          a__		:      Dict					=							None
          a__		:      Optional[int]					=							timeout

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = False)     ->		Dict:







          '''simple docstring'''

          self.text_queue.put(lowercase   ,    timeout=self.timeout)
          if stream_end:
                 self.text_queue.put(self.stop_signal   ,    timeout=self.timeout)

   def __iter__(   self)     ->		Tuple:







          '''simple docstring'''

          return self

   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          a__		:      List[Any]					=							self.text_queue.get(timeout=self.timeout)
          if value == self.stop_signal:
                 raise StopIteration()
          else:
                 return value




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


lowercase    :   Union[str, Any]						=			['bert-base-uncased', 'bert-base-cased']
lowercase    :   Any						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():




		class 					lowerCamelCase__				(							tf.keras.Model):




			'''simple docstring'''




			def __init__(   self						:List[Any]	,  a						:int		)						->					int:
										super().__init__()
										__UpperCamelCase      :  Any       =   tokenizer
										__UpperCamelCase      :  Union[str, Any]       =   AutoConfig.from_pretrained(a		)
										__UpperCamelCase      :  Dict       =   TFAutoModel.from_config(a		)







			def   _lowerCamelCase							(   self						:List[Any]	,  a						:Dict		)						->					Any:
										__UpperCamelCase      :  int       =   self.tokenizer(a		)
										__UpperCamelCase      :  int       =   self.bert(**a		)
										return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:str		)						->					str:
								super().setUp()

								__UpperCamelCase      :  Dict       =   [
								    BertTokenizer.from_pretrained(a		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								__UpperCamelCase      :  List[Any]       =   [TFBertTokenizer.from_pretrained(a		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(a	,  use_fast_bert_tokenizer=a		)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers		) == len(self.tf_tokenizers		)

								__UpperCamelCase      :  Dict       =   [
								    "This is a straightforward English test sentence.",
								    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
								    "Now we're going to add some Chinese: 一 二 三 一二三",
								    "And some much more rare Chinese: 齉 堃 齉堃",
								    "Je vais aussi écrire en français pour tester les accents",
								    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
								]
								__UpperCamelCase      :  Dict       =   list(zip(self.test_sentences	,  self.test_sentences[::-1]		)		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								for tokenizer, tf_tokenizer in zip(self.tokenizers	,  self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  str       =   tokenizer(a	,  return_tensors="tf"	,  padding="longest"		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,  tf.intaa		) == tf_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Any:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  List[str]       =   tf_tokenizer(self.paired_sentences		)
															__UpperCamelCase      :  Union[str, Any]       =   tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]	,  text_pair=[sentence[1] for sentence in self.paired_sentences]	,  )
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,  tf.intaa		) == separated_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  Optional[Any]       =   tf.function(a		)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  Tuple       =   tf.constant(a		)
																						__UpperCamelCase      :  List[str]       =   compiled_tokenizer(a		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Tuple:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  str       =   ModelToSave(tokenizer=a		)
															__UpperCamelCase      :  List[Any]       =   tf.convert_to_tensor(self.test_sentences		)
															__UpperCamelCase      :  Optional[int]       =   model(a		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						__UpperCamelCase      :  Optional[Any]       =   Path(a		) / "saved.model"
																						model.save(a		)
																						__UpperCamelCase      :  List[Any]       =   tf.keras.models.load_model(a		)
															__UpperCamelCase      :  Optional[Any]       =   loaded_model(a		)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)	,  1E-5		)